70,704 research outputs found
Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia
The declining of air quality mostly affects the elderly, children, people with asthma,
as well as a restriction on outdoor activities. Therefore, there is an importance to
provide a statistical modelling to forecast the future values of surface layer ozone (O3)
concentration. The objectives of this study are to obtain the best multivariate time
series (MTS) model and develop an online air quality forecasting system for O3
concentration in Malaysia. The implementations of MTS model improve the recent
statistical model on air quality for short-term prediction. Ten air quality monitoring
stations situated at four (4) different types of location were selected in this study. The
first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban
represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban
located in Banting, Kangar, and Tanjung Malim, also the only background station at
Jerantut. The hourly record data from 2010 to 2017 were used to assess the
characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data
of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2),
carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH)
were used to examine the best MTS models. Three methods of MTS namely vector
autoregressive (VAR), vector moving average (VMA), and vector autoregressive
moving average (VARMA), has been applied in this study. Based on the performance
error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and
Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for
VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained
the VMA(2) as the best model. The lowest root mean square error (RMSE) and
normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and
Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the
lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system
for O3 was successfully developed based on the best MTS models to represent each
monitoring station
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in
Practice on Autonomous Robots (ViPAR
Towards machine learning applied to time series based network traffic forecasting
This TFG will explore some specific use cases of the application of Machine Learning techniques to Software-Define Networks, in particular to overlay protocols such as LISP, VXLAN, etc.The aim of this project is to implement a network traffic forecasting model using time series and improve its performance with machine learning techniques, offering a better prediction based in outlier correction. This is a project developed in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC). Time Series modeling methodology is able to shape a trend and take care of any existing outlier, however it does not cover outlier impact on forecasting. In order to achieve more precision and better confidence intervals, the model combines outlier detection methodology and Artificial Neural Networks to quantify and predict outliers. A study is realized over external data to find out if there is an improvement and its effect on the predictions. Machine learning techniques as Artificial Neural Networks has proven to be an improvement of the current methodology to realize forecasting using Time Series modeling. Future work will be oriented to create an improved standard of this system focused on generalize the model.El objetivo de este proyecto es implementar un modelo de previsión de tráfico de red utilizando series temporales y mejorar su rendimiento con técnicas de aprendizaje automático, generando una mejor predicción basada en la corrección de valores atÃpicos. Se trata de un proyecto desarrollado en el Departamento de Arquitectura de Computadores (DAC) de la Universidad Politécnica de Cataluña (UPC). La metodologÃa de modelado de series temporales es capaz de predecir una tendencia y hacerse cargo de cualquier valor atÃpico ya existente, sin embargo, no cubre el impacto de estos sobre la predicción. Con el fin de lograr una mayor precisión y mejores intervalos de confianza, el modelo combina la metodologÃa de detección de valores atÃpicos y redes neuronales artificiales para cuantificar y predecir los atÃpicos. Un estudio se realiza sobre datos externos para averiguar si hay una mejora y su efecto sobre las predicciones. Las técnicas de aprendizaje automático, como redes neuronales artificiales, han demostrado ser una mejora de la metodologÃa actual para realizar la predicción utilizando modelos de series de tiempo. El trabajo futuro se orientará para crear un mejor nivel de este sistema se centró en generalizar el modelo.L'objectiu d'aquest projecte és implementar un model de previsió de trà fic de xarxa utilitzant sèries temporals i millorar el seu rendiment amb tècniques d'aprenentatge automà tic, generant una millor predicció basada en la correcció de valors atÃpics. Es tracta d'un projecte desenvolupat al Departament d'Arquitectura de Computadors (DAC) de la Universitat Politècnica de Catalunya (UPC). La metodologia de modelatge de sèries temporals és capaç de predir una tendència i fer-se cà rrec de qualsevol valor atÃpic ja existent, però, no cobreix l'impacte d'aquests sobre la predicció. Per tal d'aconseguir una major precisió i millors intervals de confiança, el model combina la metodologia de detecció de valors atÃpics i xarxes neuronals artificials per quantificar i predir els atÃpics. Un estudi es realitza sobre dades externes per esbrinar si hi ha una millora i el seu efecte sobre les prediccions. Les tècniques d'aprenentatge automà tic, com xarxes neuronals artificials, han demostrat ser una millora de la metodologia actual per a fer predicció utilitzant models de sèries de temps. El treball futur s'orientarà per crear un millor nivell d'aquest sistema es va centrar en generalitzar el model
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
In this paper, we describe a new neuro-inspired, hardware-friendly readout
stage for the liquid state machine (LSM), a popular model for reservoir
computing. Compared to the parallel perceptron architecture trained by the
p-delta algorithm, which is the state of the art in terms of performance of
readout stages, our readout architecture and learning algorithm can attain
better performance with significantly less synaptic resources making it
attractive for VLSI implementation. Inspired by the nonlinear properties of
dendrites in biological neurons, our readout stage incorporates neurons having
multiple dendrites with a lumped nonlinearity. The number of synaptic
connections on each branch is significantly lower than the total number of
connections from the liquid neurons and the learning algorithm tries to find
the best 'combination' of input connections on each branch to reduce the error.
Hence, the learning involves network rewiring (NRW) of the readout network
similar to structural plasticity observed in its biological counterparts. We
show that compared to a single perceptron using analog weights, this
architecture for the readout can attain, even by using the same number of
binary valued synapses, up to 3.3 times less error for a two-class spike train
classification problem and 2.4 times less error for an input rate approximation
task. Even with 60 times larger synapses, a group of 60 parallel perceptrons
cannot attain the performance of the proposed dendritically enhanced readout.
An additional advantage of this method for hardware implementations is that the
'choice' of connectivity can be easily implemented exploiting address event
representation (AER) protocols commonly used in current neuromorphic systems
where the connection matrix is stored in memory. Also, due to the use of binary
synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa
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